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Data analytics in the era of the ind...

Dagnino, Aldo.

Data analytics in the era of the industrial internet of things

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data analytics in the era of the industrial internet of thingsby Aldo Dagnino.
Author:	Dagnino, Aldo.
Published:	Cham :Springer International Publishing :2021.
Description:	xvii, 133 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Quantitative research.
Online resource:	https://doi.org/10.1007/978-3-030-63139-0
ISBN:	9783030631390$q(electronic bk.)

Data analytics in the era of the industrial internet of things
Dagnino, Aldo.

Data analytics in the era of the industrial internet of things[electronic resource] /by Aldo Dagnino. - Cham :Springer International Publishing :2021. - xvii, 133 p. :ill., digital ;24 cm.

Chapter 1: Industrial Internet of Things Framework -- Chapter 2: Industrial Analytics -- Chapter 3: Machine Learning to Predict Fault Events in Power Distribution Systems -- Chapter 4: Analyzing Events and Alarms in Control Systems -- Chapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants -- Chapter 6: Machine Learning Recommender for New Products and Services -- Chapter 7: Managing Analytic Projects in the IIoT Enterprise.

This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT) These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts' decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision making in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.

ISBN: 9783030631390$q(electronic bk.)

Standard No.: 10.1007/978-3-030-63139-0doiSubjects--Topical Terms:

367894
Quantitative research.

LC Class. No.: QA76.9.Q36 / D34 2021

Dewey Class. No.: 001.42

Data analytics in the era of the industrial internet of things
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505 0 $a Chapter 1: Industrial Internet of Things Framework -- Chapter 2: Industrial Analytics -- Chapter 3: Machine Learning to Predict Fault Events in Power Distribution Systems -- Chapter 4: Analyzing Events and Alarms in Control Systems -- Chapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants -- Chapter 6: Machine Learning Recommender for New Products and Services -- Chapter 7: Managing Analytic Projects in the IIoT Enterprise.
520 $a This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT) These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts' decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision making in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.
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